Enhancing sparsity in linear prediction of speech by iteratively reweighted 1-norm minimization

Linear prediction of speech based on 1-norm minimization has already proved to be an interesting alternative to 2-norm minimization. In particular, choosing the 1-norm as a convex relaxation of the 0-norm, the corresponding linear prediction model offers a sparser residual better suited for coding applications. In this paper, we propose a new speech modeling technique based on reweighted 1-norm minimization. The purpose of the reweighted scheme is to overcome the mismatch between 0-norm minimization and 1-norm minimization while keeping the problem solvable with convex estimation tools. Experimental results prove the effectiveness of the reweighted 1-norm minimization, offering better coding properties compared to 1-norm minimization.

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Daniele Giacobello, Mads Græsbøll Christensen, Manohar N. Murthi, Søren Holdt Jensen, Marc Moonen
• Keywords: 1-norm Minimization, 2-norm Minimization, ICASSP 2010, Linear Prediction, Signal Processing